Privacy Preserving and Truthful Detection of Packet Dropping Attacks(2015)

Note: Please Scroll Down to See the Download Link.

INTRODUCTION

 

IN a multi-hop wireless network, nodes cooperate in relaying/ routing traffic. An adversary can exploit this cooperative nature to launch attacks. For example, the adversary may first pretend to be a cooperative node in the route discovery process. Once being included in a route, the adversary starts dropping packets. In the most severe form, the malicious node simply stops forwarding every packet received from upstream nodes, completely disrupting the path between the source and the destination. Eventually, such a severe denial-of-service (DoS) attack can paralyze the network by partitioning its topology. Even though persistent packet dropping can effectively degrade the performance of the network, from the attacker’s standpoint such an “always-on” attack has its disadvantages. First, the continuous presence of extremely high packet loss rate at the malicious nodes makes this type of attack easy to be detected [25]. Second, once being detected, these attacks are easy to mitigate. For example, in case the attack is detected but the malicious nodes are not identified, one can use the randomized multi-path routing algorithms

[28], [29] to circumvent the black holes generated by the attack, probabilistically eliminating the attacker’s threat. If the malicious nodes are also identified, their threats can be completely eliminated by simply deleting these nodes from the network’s routing table. A malicious node that is part of the route can exploit its knowledge of the network protocol and the communication context to launch an insider attack—an attack that is intermittent, but can achieve the same performance degradation effect as a persistent attack at a much lower risk of being detected. Specifically, the malicious node may evaluate the importance of various packets, and then drop the small amount that are deemed highly critical to the operation of the network. For example, in a frequency-hopping network, these could be the packets that convey frequency hopping sequences for network-wide frequency-hopping synchronization; in an ad hoc cognitive radio network, they could be the packets that carry the idle channel lists (i.e., white spaces) that are used to establish a network-wide control channel. By targeting these highly critical packets, the authors in [21], [24], [25] have shown that an intermittent insider attacker can cause significant damage to the network with low probability of being caught. In this paper, we are interested in combating such an insider attack. In particular, we are interested in the problem of detecting the occurrence of selective packet drops and identifying the malicious node(s) responsible for these drops. Detecting selective packet-dropping attacks is extremely challenging in a highly dynamic wireless environment. The difficulty comes from the requirement that we need to not only detect the place (or hop) where the packet is dropped, but also identify whether the drop is intentional or unintentional. Specifically, due to the open nature of wireless medium, a packet drop in the network could be caused by harsh channel conditions (e.g., fading, noise, and interference, a.k.a., link errors), or by the insider attacker. In an open wireless environment, link errors are quite significant, and may not be significantly smaller than the packet dropping rate of the insider attacker. So, the insider attacker can camouflage under the background of harsh channel conditions. In this case, just by observing the packet loss rate is not enough to accurately identify the exact cause of a packet loss. The above problem has not been well addressed in the literature. As discussed in Section 2, most of the related works preclude the ambiguity of the environment by assuming that malicious dropping is the only source of packet loss, so that there is no need to account for the impact of link errors. On the other hand, for the small number of works that differentiate between link errors and malicious packet drops, their detection algorithms usually require the number of maliciously-dropped packets to be significantly higher than link errors, in order to achieve an acceptable detection accuracy. In this paper, we develop an accurate algorithm for detecting selective packet drops made by insider attackers. Our algorithm also provides a truthful and publicly verifiable decision statistics as a proof to support the detection decision. The high detection accuracy is achieved by exploiting the correlations between the positions of lost packets, as calculated from the auto-correlation function (ACF) of the packet-loss bitmap—a bitmap describing the lost/received status of each packet in a sequence of consecutive packet transmissions. The basic idea behind this method is that even though malicious dropping may result in a packet loss rate that is comparable to normal channel losses, the stochastic processes that characterize the two phenomena exhibit different correlation structures (equivalently, different patterns of packet losses). Therefore, by detecting the correlations between lost packets, one can decide whether the packet loss is purely due to regular link errors, or is a combined effect of link error and malicious drop. Our algorithm takes into account the cross-statistics between lost packets to make a more informative decision, and thus is in sharp contrast to the conventional methods that rely only on the distribution of the number of lost packets. The main challenge in our mechanism lies in how to guarantee that the packet-loss bitmaps reported by individual nodes along the route are truthful, i.e., reflect the actual status of each packet transmission. Such truthfulness is essential for correct calculation of the correlation between lost packets. This challenge is not trivial, because it is natural for an attacker to report false information to the detection algorithm to avoid being detected. For example, the malicious node may understate its packet-loss bitmap, i.e., some packets may have been dropped by the node but the node reports that these packets have been forwarded. Therefore, some auditing mechanism is needed to verify the truthfulness of the reported information. Considering that a typical wireless device is resource-constrained, we also require that a user should be able to delegate the burden of auditing and detection to some public server to save its own resources. Our solution to the above public-auditing problem is constructed based on the homomorphic linear authenticator (HLA) cryptographic primitive [2], [3], [27], which is basically a signature scheme widely used in cloud computing and storage server systems to provide a proof of storage

from the server to entrusting clients [30]. However, direct application of HLA does not solve our problem well, mainly because in our problem setup, there can be more than one malicious node along the route. These nodes may collude (by exchanging information) during the attack and when being asked to submit their reports. For example, a packet and its associated HLA signature may be dropped at an upstream malicious node, so a downstream malicious node does not receive this packet and the HLA signature from the route. However, this downstream attacker can still open a back-channel to request this information from the upstream malicious node. When being audited, the downstream malicious node can still provide valid proof for the reception of the packet. So packet dropping at the upstream malicious node is not detected. Such collusion is unique to our problem, because in the cloud computing/storage server scenario, a file is uniquely stored at a single server, so there are no other parties for the server to collude with. We show that our new HLA construction is collusion-proof. Our construction also provides the following new features. First, privacy-preserving: the public auditor should not be able to decern the content of a packet delivered on the route through the auditing information submitted by individual hops, no matter how many independent reports of the auditing information are submitted to the auditor. Second, our construction incurs low communication and storage overheads at intermediate nodes. This makes our mechanism applicable to a wide range of wireless devices, including low-cost wireless sensors that have very limited bandwidth and memory capacities. This is also in sharp contrast to the typical storage-server scenario, where bandwidth/storage is not considered an issue. Last, to significantly reduce the computation overhead of the baseline constructions so that they can be used in computation-constrained mobile devices, a packet-block-based algorithm is proposed to achieves scalable signature generation and detection. This mechanism allows one to trade detection accuracy for lower computation complexity. The remainder of this paper is organized as follows. In Section 2 we review the related work. The system/adversary models and problem statement are described in Section 3. We present the proposed scheme and analyze its security performance and overheads in Section 4. The low-computation- overhead block-based algorithm is proposed in Section 5. Simulation results are presented in Section 6, and we conclude the paper in Section 7.

IMPLEMENTATION

 

Service Provider:

In this module, the service provider browses the file and sends to the particular end users via router. And also service provider can assign energy and assign distances for the nodes in router.

Router:

In this module, the router sends the file from source to destination (from service provider to end users) by selecting shortest distances between two nodes & sufficient node energy. And if node has less energy than file size then packet dropper in router drops the some packets from file and sends remaining file to the destination. And it can also do some operations like view distances, view energy, view files, view attackers, verify, refresh.

Auditor:

In this module, the auditor discovers the traffic pattern, means it stores the details of dropped packets. It contains details of in which node packets are dropped, how many no of packets dropped, from which file dropped & status of packets.

Destination (End User ):

In this module, there are n no of destinations (A, B, C….). These end users only receive the file from service provider via router. While getting the file from service provider there may be chances of packets dropping, if packets are dropped then end user will gets dropped packets from point to point manager. The end users receive the file by without changing the File Contents. Users may receive particular data files within the network only.

Attacker:

Attacker is one who makes changes the energy of particular nodes in router. And all attackers’ details stored in router with their all details such as attacker Ip address, attacked node, modified energy and attacked time.

 

 

Click here to download Privacy Preserving and Truthful Detection of Packet Dropping Attacks(2015) source code